Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes.

The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

The predictive validity of RETTS-HEV as an acuity triage tool in the emergency department of a Danish Regional Hospital.

INTRODUCTION: The Rapid Emergency Triage and Treatment System - Hospital Unit West (RETTS-HEV) is a triage system used in the emergency department (ED) in Herning, Denmark, since 2010. It categorizes patients according to priority and defines a time limit on how long patients can wait before being seen by a doctor depending on the severity of their condition. The purpose of this study was to determine the predictive validity of RETTS-HEV by measuring the association between triage scores and outcomes such as the admission rate, the length of stay (LOS), and mortality. MATERIALS AND METHODS: We performed an observational cohort study by examining the medical records of all patients who attended the ED from 1 September 2012 to 30 November 2012, at the Regional Hospital West Jutland in Herning, Denmark (N=4680). We defined the following outcomes to make associations with the patients' triage category: in-hospital mortality, and 30, 60, and 90-day mortalities, the hospital LOS and the admission rate, on the basis of complete information from the Danish National Patient Registry. RESULTS: The distribution of age, comorbidity, admission, LOS, and mortality over triage categories differed as expected. After making adjustments for these differences, we found a consistent association between triage categories and in-hospital mortality, and 30, 60, and 90-day mortalities, the hospital LOS, and the admission rate. CONCLUSION: RETTS-HEV was found to be closely related to all examined outcomes, and therefore useful in the risk stratification of ED patients.

Assessing time to treatment and patient inflow in a Danish emergency department: a cohort study using data from electronic emergency screen boards.

BACKGROUND: The purpose of this study was to assess and describe the patient inflow during a 1-month period in a Danish emergency department and to evaluate if the intended times to treatment (TTT) related to category of triage were met. METHODS: Data from electronic emergency screen boards were extracted from the 1st to the 30th of April 2013. 2000 patients were enrolled of which 1011 were eligible for inclusion in the study of TTT. Patient inflow was described according to hours of the day and days of the week. Patients were divided into groups of triage and TTT was assessed in the different groups. Adjusted odds ratios of not being seen on time were calculated between triage groups and time of the day/week. RESULTS: The pattern of inflow differed between weekdays and weekends. On weekdays it peaked around midday and on weekends it peaked during the late afternoon/evening. The distributions of the different triage categories between days were similar. Monday had the most patient contacts while Saturday showed the least. Category II (orange) patients were the most prone to exceed the intended TTT. The risk of not being seen on time when compared to daytime, was on evenings OR 2.3 [1.1;4.9] and on nights OR 2.0 [1.2;3.9]. On weekends the odds ratio was OR 1.9 [0.8;4.7] compared to weekdays. CONCLUSION: The results demonstrated varying patterns of patient inflow between weekdays and weekends. There was a significantly increased risk of being attended late when arriving on evenings and nights. Likewise higher acuity was associated with exceeded TTT.